Beyond Homology Transfer: Deep Learning for Automated Annotation of Proteins

[agitter]

https://doi.org/10.1101/168120 (http://www.biorxiv.org/content/early/2017/07/25/168120)

Accurate annotation of protein functions is important for a profound understanding of molecular biology. A large number of proteins remain uncharacterized because of the sparsity of available supporting information. For a large set of uncharacterized proteins, the only type of information available is their amino acid sequence. In this paper, we propose DeepSeq -- a deep learning architecture -- that utilizes only the protein sequence information to predict its associated functions. The prediction process does not require handcrafted features; rather, the architecture automatically extracts representations from the input sequence data. Results of our experiments with DeepSeq indicate significant improvements in terms of prediction accuracy when compared with other sequence-based methods. Our deep learning model achieves an overall validation accuracy of 86.72%, with an F1 score of 71.13%. Moreover, using the automatically learned features and without any changes to DeepSeq, we successfully solved a different problem i.e. protein function localization, with no human intervention. Finally, we discuss how this same architecture can be used to solve even more complicated problems such as prediction of 2D and 3D structure as well as protein-protein interactions.

I'm opening an issue to note the paper, but I don't plan to add it to the review.

[recluze]

Hi,

I'm an author and would be happy to answer questions about this if anyone is interested.

[agitter]

@recluze thanks for offering. Discussing papers with their authors has been one of the best parts of this review.

Note that my comment above about adding it to the review doesn't reflect on your work directly. Primarily, our review manuscript is far too long so I am reluctant to add new topics without good cause.